Abstract
In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in data mining post-processing, which is one of the key processes in a data mining process. It is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis data mining result, the hepatitis data mining results and rule sets from the eight UCI datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abe, H., Yamaguchi, T.: Constructive Meta-learning with Machine Learning Method Repositories. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 502–511. Springer, Heidelberg (2004)
Abe, H., Ohsaki, M., Yokoi, H., Yamaguchi, T.: Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis. In: Washio, T., Sakurai, A., Nakajima, K., Takeda, H., Tojo, S., Yokoo, M. (eds.) JSAI Workshop 2006. LNCS (LNAI), vol. 4012, pp. 425–435. Springer, Heidelberg (2006)
Ali, K., Manganaris, S., Srikant, R.: Partial Classification Using Association Rules. In: Proc. of Int. Conf. on Knowledge Discovery and Data Mining KDD 1997, pp. 115–118 (1997)
Booker, L.B., Holland, J.H., Goldberg, D.E.: Classifier Systems and Genetic Algorithms. Artificail Inteligence 40, 235–282 (1989)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, pp. 255–264 (1997)
Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using model trees for classification. Machine Learning 32(1), 63–76 (1998)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proc. of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. of Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. In: Proc. of Euro. Conf. on the Principles of Data Mining and Knowledge Discovery PKDD 1998, pp. 19–27 (1998)
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. Springer Series in Statistics, vol. 1. Springer, Heidelberg (1979)
Gray, B., Orlowska, M.E.: CCAIIA: Clustering Categorical Attributes into Interesting Association Rules. In: Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining PAKDD 1998, pp. 132–143 (1998)
Hamilton, H.J., Shan, N., Ziarko, W.: Machine Learning of Credible Classifications. In: Proc. of Australian Conf. on Artificial Intelligence AI 1997, pp. 330–339 (1997)
Hatazawa, H., Negishi, N., Suyama, A., Tsumoto, S., Yamaguchi, T.: Knowledge Discovery Support from a Meningoencephalitis Database Using an Automatic Composition Tool for Inductive Applications. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 28–33. Springer, Heidelberg (2000)
Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measure of Interest. Kluwer Academic Publishers, Dordrecht (2001)
Hinton, G.E.: Learning distributed representations of concepts. In: Proc. of 8th Annual Conference of the Cognitive Science Society, Amherest, MA (1986) REprinted in R.G.M.Morris (ed.)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)
Klösgen, W.: Explora: A Multipattern and Multistrategy Discovery Assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI/MIT Press, California (1996)
Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The AQ15 Inductive Learning System: An Over View and Experiments, Reports of Machine Learning and Inference Laboratory, No.MLI-86-6, George Mason University (1986)
Mitchell, T.M.: Generalization as Search. Artificial Intelligence 18(2), 203–226 (1982)
Ohsaki, M., Sato, Y., Kitaguchi, S., Yokoi, H., Yamaguchi, T.: Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 1072–1081. Springer, Heidelberg (2004)
Ohsaki, M., Kitaguchi, S., Kume, S., Yokoi, H., Yamaguchi, T.: Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 362–373. Springer, Heidelberg (2004)
Piatetsky-Shapiro, G.: Discovery, Analysis and Presentation of Strong Rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)
Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Quinlan, J.R.: Induction of Decision Tree. Machine Learning 1, 81–106 (1986)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
Rijsbergen, C.: Information Retrieval, ch. 7. Butterworths, London (1979), http://www.dcs.gla.ac.uk/Keith/Chapter.7/Ch.7.html
Smyth, P., Goodman, R.M.: Rule Induction using Information Theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. AAAI/MIT Press (1991)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proc. of Int. Conf. on Knowledge Discovery and Data Mining KDD 2002, pp. 32–41 (2002)
Witten, I.H., Frank, E.: DataMining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Wolpert, D.: Stacked Generalization. Neural Network 5(2), 241–260 (1992)
Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining PAKDD 1999, pp. 479–488 (1999)
Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining. IEEE Trans. on Knowledge and Data Engineering 15(4), 952–960 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abe, H., Tsumoto, S., Ohsaki, M., Yokoi, H., Yamaguchi, T. (2006). Evaluating Learning Algorithms with Meta-learning Schemes for a Rule Evaluation Support Method Based on Objective Indices. In: Hoffmann, A., Kang, Bh., Richards, D., Tsumoto, S. (eds) Advances in Knowledge Acquisition and Management. PKAW 2006. Lecture Notes in Computer Science(), vol 4303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11961239_7
Download citation
DOI: https://doi.org/10.1007/11961239_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68955-3
Online ISBN: 978-3-540-68957-7
eBook Packages: Computer ScienceComputer Science (R0)